In this article, smart water-energy management based on an artificial neural network for a photovoltaic-wind system coupled with water pumping and reverse osmosis desalination unit with hydraulic storage is developed. The proposed system was designed to meet freshwater demand for remote areas of Tunisia's south. The authors focus on an artificial neural network-type management strategy, ensuring power-sharing between sources, hydraulic components, and tank storage. The main objective of the management strategy is to deliver and purify as much freshwater as possible by taking advantage of all the energy available according to climatic conditions. A parametric sensitivity algorithm is developed to find the most appropriate neural network architecture. A dynamic simulator for 1 year of operation integrates the energy management of the system is developed. The used water-energy management-based artificial neural network showed good results of power-sharing during a fast time with a correlation coefficient and a coefficient of determination between the actual and estimated values of the three motor pumps electrical power equal to 96% and 98%, respectively. Also, the root mean squared error was acceptable at 0.1476. These results have been proved over a year and can be used by other researchers with a similar system.

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Coupling renewable energy sources with reverse osmosis desalination systems offers a suitable solution to freshwater scarcity problem, especially in regions suffering from hydraulic and electrical crisis. In this context, design, feasibility and performance evaluation of a standalone brackish water reverse osmosis desalination unit powered with hybrid PV/Wind sources in isolated community have been investigated. In order to reduce the cost of investment and maintenance, the proposed system was based on hydraulic storage in a water tower instead of electrochemical storage. This study aims to optimize the power sharing of the generated power between the motor-pumps while respecting constraints and maximize the produced freshwater quantity. For this purpose, a genetic algorithm optimization of the fuzzy-logic membership functions is proposed. First, a simple fuzzy logic system was used to improve the power sharing between the hydro mechanical processes. Then, an offline optimization of the developed fuzzy-logic using genetic algorithm was applied. Finally, to validate the developed optimal energy management strategy performance, single fuzzy-logic and hybrid fuzzy logic-genetic algorithm energy management system results are compared for a real power profiles. The optimized fuzzy-logic energy management strategy demonstrated its high performance in improving the system energy efficiency with the reduction of the consumed energy (gain of 7.1% in October (Autonm)), and enhance the permeate water production (gain of 2.96% during a week) compared with the energy management based only fuzzy-logic strategy.

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